U.S. patent application number 15/155650 was filed with the patent office on 2016-09-22 for wireless communication network using multiple key performance indicators and deviations therefrom.
The applicant listed for this patent is P. I. Works TR Bilisim Hizm. San. ve Tic A.S.. Invention is credited to Djakhongir Siradjev, Serkan Sofuoglu.
Application Number | 20160277946 15/155650 |
Document ID | / |
Family ID | 56925605 |
Filed Date | 2016-09-22 |
United States Patent
Application |
20160277946 |
Kind Code |
A1 |
Sofuoglu; Serkan ; et
al. |
September 22, 2016 |
Wireless Communication Network Using Multiple Key Performance
Indicators and Deviations Therefrom
Abstract
A system and method for dynamically improving or optimizing the
performance and robustness of a wireless communication network such
as a mobile communication system or cellular telephony network are
disclosed. In some aspects, a plurality of time and space dependent
key performance indicators (KPI) are used as part of a statistical
determination of a pattern and schedule for optimizing the design,
configuration and operation of the network. By dynamically applying
a method of multiple KPI deviations (MKD) the system and method
improves handover execution in cellular or similar systems and
reduces radio link failures and improves overall subscriber service
quality.
Inventors: |
Sofuoglu; Serkan;
(Pleasanton, CA) ; Siradjev; Djakhongir;
(Istanbul, TR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
P. I. Works TR Bilisim Hizm. San. ve Tic A.S. |
Istanbul |
|
TR |
|
|
Family ID: |
56925605 |
Appl. No.: |
15/155650 |
Filed: |
May 16, 2016 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
14325467 |
Jul 8, 2014 |
|
|
|
15155650 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 36/00837 20180801;
H04W 24/02 20130101; H04W 28/18 20130101 |
International
Class: |
H04W 24/02 20060101
H04W024/02; H04W 28/18 20060101 H04W028/18 |
Claims
1. A computer-implemented method for improving the performance of a
computerized Mobile Communication System (MCS) in a multi-cell
network, comprising: in a computer comprising a processor, the
computer in communication with the MCS: gathering baseline wireless
communication signal data for a target area in which the method is
to be carried out, including areas containing wireless
communication cells or wireless communication cell relations for
which wireless communication cell or wireless communication cell
relation performance characteristics are to be extracted based on a
plurality of key performance indicators (KPI), and storing baseline
data in a computer-readable database; seeding the target area by
applying a first solution set to a first wireless communication
cell and a second solution set to a second wireless communication
cell; measuring at least one performance metric for said first and
second wireless communication cells; storing data representing said
at least one performance metric for said first and second wireless
communication cells in said database; calculating a unified
performance metric for said first wireless communication cell and
for said wireless communication second cell, the unified
performance metric calculated by (a) calculating respective KD
values for each of said at least one performance metric for said
first wireless communication cell and for said second wireless
communication cell and (b) calculating a MKD value for said first
wireless communication cell and for said second wireless
communication cell, said MKD value comprising a sum of said
respective KD values for said respective first or second wireless
communication cells; identifying a maximum MKD value and a
corresponding wireless communication cell; applying the solution
set of the corresponding cell to the other wireless communication
cell; and generating cellular signals from a respective base
station in the first and second wireless communication cells using
the solution set of the corresponding wireless communication
cell.
2. The method of claim 1, further comprising: identifying a minimum
MKD value and a second corresponding wireless communication cell;
rolling back settings on the second corresponding wireless
communication cell to a baseline solution set if said minimum MKD
value is less than a predetermined minimum value; and generating
cellular communication signals from a base station in the second
corresponding wireless communication cell using the baseline
solution set.
3. The method of claim 1, further comprising iteratively and
automatically repeating said method on a periodic basis.
4. A computer system that improves the performance of a Mobile
Communication System (MCS) in a multi-cell network, comprising: a
processor of said computer system, in communication with the MCS: a
wireless communication signal receiver that gathers baseline
wireless communication signal data for a target area, disposed in
areas containing wireless communication cells or wireless
communication cell relations of said MCS for which wireless
communication cell or wireless communication cell relation
performance characteristics are to be extracted based on a
plurality of key performance indicators (KPI); a computer-readable
database, in data communication with said processor, that stores
baseline data received by said receiver; a solution engine
implemented in said processor and configured and arranged to
execute instructions that seed the target area by applying a first
solution set to a first wireless communication cell and a second
solution set to a second wireless communication cell; said computer
system further configured and arranged to detect at least one
performance metric for said first and second wireless communication
cells using said wireless communication signal receiver; said
computer system further configured and arranged to store data
representing said at least one performance metric for said first
and second wireless communication cells in said database; said
processor configured and arranged to calculate a unified
performance metric for said first wireless communication cell and
for said wireless communication second cell, the unified
performance metric calculated by (a) calculating respective KD
values for each of said at least one performance metric for said
first wireless communication cell and for said second wireless
communication cell and (b) calculating a MKD value for said first
wireless communication cell and for said second wireless
communication cell, said MKD value comprising a sum of said
respective KD values for said respective first or second wireless
communication cells; said processor configured and arranged to
identify a maximum MKD value and a corresponding wireless
communication cell; said processor configured and arranged to apply
the solution set of the corresponding cell to the other wireless
communication cell; and said processor configured and arranged to
generate cellular signals from a respective base station in the
first and second wireless communication cells using the solution
set of the corresponding wireless communication cell.
Description
RELATED APPLICATIONS
[0001] This application claims priority to and is a continuation in
part of U.S. Application Ser. No. 14/325,467, filed on Jul. 8,
2014, entitled "Wireless Communication Network Performance and
Robustness Tuning and Optimization Using Deviations in Multiple Key
Performance Indicators," which is hereby incorporated by
reference.
TECHNICAL FIELD
[0002] The present disclosure generally relates to the field of
wireless communication networks. More specifically, it relates to
dynamically improving or optimizing the performance and robustness
of such networks using a plurality of key performance indicators
(KPI) as well as data gathering and statistical techniques to
analyze multiple KPI deviations (MKD).
BACKGROUND
[0003] Implementing wireless communication networks in real life
environments is typically a challenging and complex undertaking.
The complexities of such networks arise from numerous factors. One
set of factors includes the physical communication channels in the
presence of urban structures, natural terrain, atmospheric
variations and other environmental factors. Another set of factors
arises from the engineering systems needed to support wireless
communications over useful ranges, which includes the antenna
designs and placements, communication base station hardware and
software, wired communication infrastructure, switching and other
maintenance and upkeep factors. Yet another set of factors arises
from the mobile wireless devices and their sheer numbers in some
areas, each requiring real-time and acceptable quality of service
around the clock. Taken together, the infrastructure and devices
and techniques used to interconnect the parts of the system can be
referred to as a mobile communication system (sometimes "MCS"). A
primary goal of MCS system designers and operators is to implement
and operate the MCS system in the most reliable, robust and
efficient manner so as to serve the largest number of customers
with the highest level of quality at a most cost effective
rate.
[0004] One example of MCS is cellular telephone communication
systems and networks, which vary from region to region but share
physical and design and performance features. These systems
generally include a network of base stations including telephony
processors and servers coupled to physical antenna installations.
The antenna installations permit over the air wireless
communication with suitably equipped and subscribing customers. In
most or all cases, a mobile communication device can continue a
communication session even when traversing from one cell of the
cellular network to another using established handover methods. A
well designed and operated cellular system offers consistent good
quality communication with few communication problems (dropped
calls) or disruptions due to handover events, interference, fading
or other noise generating factors. The settings of various
controlling parameters in mobile communication systems (MCS)
significantly affect various dimensions of performance of mobile
devices, which are connected to and utilize the services provided
by the MCS. In the prior art MCS and prior art standards and
practices used to govern the MCS, improvements to such performance
of mobile devices under conditions of mobility are referred to as
"mobility robustness" improvements, which seek to improve the
success rates of handover of the mobile device from one cell to
another in the MCS and eventually improve drop call rates.
[0005] Base Stations are network elements to which mobile user
devices are connected in the MCS using radio channels. Handover is
a mechanism of the MCS whereby a user mobile device is assigned
different serving Base Stations to connect to as the mobile user
devices move around the coverage areas of a MCS. Due to high number
of relations defined in a typical MCS, manual setting of handover
(HO) parameters in current 2G/3G/4G systems is considered too
costly and time consuming task. In scenarios where manual
configuration is done, incorrect or unoptimized HO parameter
settings negatively affect user experience and waste network
resources by causing HO ping-pongs, HO failures, and radio link
failures (RLF). While HO failures that do not lead to radio link
failures (RLF) are often recoverable and transparent to the user,
RLFs caused by incorrect HO parameter settings have a combined
impact on user experience and on the availability of network
resources.
[0006] A number of metrics are defined to characterize the
performance or robustness of a MCS. The metrics are referred to as
Key Performance Indicators (KPI). However, merely defining such
metrics does not help improve the performance and robustness of
networks, especially in dynamic conditions that are subject to time
variation. The art lacks well-studied and reliable ways to predict
and account for such dynamic network conditions. There have been
various attempts to provide solutions to achieving maximum
performance efficiency of the MCS.
[0007] US-2005/0064820 purports to disclose analyzing of a
wired/wireless network and to optimize performance of the network
by gathering data continuously from elements constituting a wired
or wireless network to find an element of which performance and
efficiency deteriorates. An optimal plan to resolve low performance
is chosen through data analysis.
[0008] US-2007/0002759 purports to disclose a method for monitoring
system conditions for time periods within a periodic time interval
within which network parameters for optimizing a wireless may be
determined.
[0009] US-2013/0143561 purports to disclose a computing platform
provided to enable optimizing a cellular network by gathering data,
retrieve statistical KPIs from a plurality of network elements,
generate a predictive Key Performance Indicator by correlating
information from the network elements and retrieved KPIs, and
trigger changes to the cellular network based on the predicted
trend.
[0010] US-2006/0063521 purports to disclose system monitoring and
fault detection capable of detecting a sleeping cell, for example,
by determining a deviation between actual cell performance and an
expected cell performance.
[0011] US-2007/0026810 purports to disclose a wireless
communication terminal that communicates on a plurality of
sub-carriers divided into a plurality of frequency bands, wherein
each frequency band includes at least one sub-carrier. The terminal
measures a channel quality indicator (CQI) for a plurality of
frequency bands, identifies a subset of frequency bands for which
the channel quality indicator has been measured based on a subset
criterion, and transmits a report identifying a subset of frequency
bands for which a channel quality indicator has been measured or
frequency bands not in the subset.
[0012] US-2011/0151881 purports to disclose methods and systems for
fractional frequency reuse in wireless networks. A reuse factor of
one (f=1) may be used to serve mobile stations located in inner
cell regions that do not experience significant inter-cell
interference (ICI) and a reuse factor of less than one (f<1) may
be used for mobile stations located near the cell edge that tend to
experience higher levels of ICI. Dynamic allocation of frequency
partitions and adjustment of power levels for each base station
sector are provided in order to avoid collisions between
neighboring base station sectors and achieve improved capacity and
performance. Load balancing may also be provided.
[0013] US-2011/0294527 purports to disclose a system that varies
parameters in order to optimize wireless performance of cellular
networks. The system is based on extended ANR (Automatic cell
Neighbor relations) functionality as a means for generating cluster
information in an electronic device and to transmit clustering
information to one or more base stations. The disclosure emphasizes
interference reduction techniques and the need for (dynamic)
clustering of wireless network entities.
[0014] US-2012/0115423 purports to disclose a method that varies
parameters in order to optimize wireless performance of cellular
networks. It shows a frequency deviation pre-calibration method
comprising estimating an uplink frequency deviation value of a user
equipment and acquiring a historical uplink frequency deviation
pre-calibration value, determining from the historical uplink
frequency deviation pre-calibration value a current uplink
frequency deviation pre-calibration value of the user equipment
which is closer to the estimated uplink frequency deviation value
than the historical uplink frequency deviation pre-calibration
value and performing frequency deviation pre-calibration on the
user equipment with the current uplink frequency deviation
pre-calibration value.
[0015] US-2012/0282933 purports to disclose a controller coupled to
a mobile communications environment including at least one of a
public and a private network and method of controlling a mobile
device in the mobile communications environment. The controller
includes a receiver that receives data about network operating
parameters at specific locations within the at least one of a
public and private network, a processor that evaluates the data
about the network operating parameters at the specific locations
based upon rules for the mobile device, and a transmitter that
sends advisories to a mobile device located within a predetermined
proximity to one of the specific locations about the network
operating parameters.
[0016] US-2012/0322438 purports to disclose an Operating Support
System for Performance Management of a mobile telecommunications
system comprising a plurality of nodes and radio access units
servicing a plurality of cells generating a plurality of
operational events data and counter values measured periodically
for a first Result Output Period, ROP. Events data and counter
values originating from the nodes and radio access units are
collected, aggregated periodically for a second and further ROPs
having a duration longer than the first ROP. From the collected
events data further counter values are created periodically for the
second and further ROPs. The aggregated and further counter values
are processed corresponding to the originating nodes, radio access
units and ROP, and the processed counter values are analyzed for
providing system operational performance indicia in different time
scales.
[0017] Prior art solutions do not provide an adequate solution to
the problem of optimization of the MCS on the dimension of mobility
performance while at the same time allowing maximum improvements to
be achieved to other measures of network performance such as data
transfer efficiency.
SUMMARY
[0018] An objective of mobility robustness optimization (MRO) is
reducing the number of handover(HO) related radio link failures
(RLF). Furthermore, non-optimal configuration of handover
parameters, even if it does not result in RLFs, may lead to serious
degradation of the service performance. An example of such a
situation is incorrect setting of the HO offset parameter, which
may cause a ping-pong effect (bouncing rapidly between connections
with different neighboring Base Stations) or prolonged connection
to a non-optimal cell. Another objective is the reduction of the
inefficient use of network resources due to unnecessary or missed
handovers, which can result from failures due to too late HO
triggering, too early HO triggering, and/or HO to an incorrect
cell.
[0019] Accordingly, aspects of this invention are directed to using
certain mobility parameters that are monitored and modified to
optimize or improve the performance of wireless communication
services in a computerized MCS. Configuration parameters are
selected, changed, and the impact of such changes on multiple key
performance indicators (KPI) are monitored according to a
particular pattern and schedule, using the mechanism of multiple
KPI deviations (MKD). A well-defined set of mobility robustness
optimization (MRO) changes are applied on selected badly performing
regions of the MCS for a specified period of time with the goal of
improving handover execution success by reducing radio link
failures and with controlled impact on specific services (in an
example, down-link data throughput). Results can then be evaluated
to find the optimum set of changes to achieve performance
improvements of the steady-state behavior of the MCS.
[0020] In an aspect of this invention, configuration parameters may
be composed of cell-level parameters (e.g. handover parameter
offsets, hysteresis, thresholds, time-to-trigger values) and/or
cell relational level parameters (e.g. cell individual offsets,
idle mode reselection offsets) as specified in prior art MCS
standardization group (i.e., the 3GPP Organization) Specifications.
(e.g., 3GPP TS 28.628 clause 4.3.2). A "Solution set" refers to one
or a combination of configuration parameters. "Rollback" refers to
changing back the set of configuration parameters to previous
values that were set before the changes were applied
[0021] In an aspect, the present concepts can be applied, for
example, but not only, to multi-technology MCS (e.g., Third
Generation and Fourth Generation MCS) using a closed-loop
optimization processor to improve numerous operating parameters
such as downlink data throughput, handover success rates, and other
factors.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] For a fuller understanding of the nature and advantages of
the present concepts, reference is made to the following detailed
description of preferred embodiments and in connection with the
accompanying drawings, in which:
[0023] FIG. 1 schematically illustrates a MRO MKD according to an
embodiment; configuration; showing the Radio Access Network
entities (RAN) connected to the Operational support system
containing MCS configuration parameters and real-time metrics
gathered by and about the MCS. The MRO MKD processor is the locus
of one embodiment of the present method disclosed herein;
[0024] FIG. 2 illustrates an exemplary architecture and system for
carrying out the present method; showing schematic elements of the
method and system described herein;
[0025] FIG. 3 illustrates a process for monitoring, learning and
applying optimum solution sets to multiple KPI in a MCS;
[0026] FIG. 4 illustrates exemplary phases of MRO MKD operation and
exemplary timeframe of operation of an embodiment of the method
described herein; and
[0027] FIG. 5 illustrates exemplary table showing the system's use
of multiple KPIs in a multi-KPI deviation (MKD) architecture.
DETAILED DESCRIPTION
[0028] The operator of the MCS is continually seeking new
techniques for running their dynamic and complex networks at
maximum efficiency. Performance optimization techniques are
employed to make systematic changes to performance-affecting
parameters stored in the network in order to ensure the best
possible performance for users of the various services provided by
the operator, such as mobility performance of users and data
transfer speeds. Indicators (e.g., KPI) are used to determine
whether such optimization is needed for parts of the network. These
indicators are typically referred to as KPI, which can take the
form of formulae composed of performance measurements combined in
certain ways to better show the quality of various services along
various dimensions of performance. The underlying factors of each
KPI are stored in the operational support system (OSS, e.g., OSS
120 in FIG. 1) processor managed by the MCS operator. Optimization
processes collect the appropriate KPI data, combine this
measurement data into formulae, and evaluate the formulae according
to a certain schedule to determine whether the certain network
services are operating at maximum efficiency. Changes to the
performance-affecting parameters will change the value of the KPI.
There are typically many KPI used to determine quality levels of
the network. These performance-affecting parameters interact with
each other in complex ways and impact the KPI in complex ways. The
optimization problem can be defined as a set of techniques to
change performance-affecting parameters to achieve desired results
of improving certain dimensions of performance of the MCS.
[0029] One problem addressed by embodiments of the systems and
methods disclosed herein is to make use of a multiple KPI
evaluation on a baseline trend. In particular, dynamic calculation
of deviation in terms of multiple KPIs in comparison with a
baseline data set may be realized according to embodiments of
systems and methods herein rather than static or pre-defined
thresholds per KPI without any adaptation to the network
situation.
[0030] The prior art, such as the references described in the
Background, do not provide adequate solutions to problems such as
addressed by aspects of the present invention for an automatic
mechanism to optimize the performance of the MCS on the dimension
of mobility performance, while at the same time allowing maximum
improvements to be achieved to other measures of network
performance such as data transfer efficiency.
[0031] It should be noted that well-defined target KPIs such as
Handover Failures, Attempts, etc. may be used by MRO
implementations as described in prior art in the 3GPP, MCS
standardization specifications (e.g. 3GPP TS 28.628 clause 4.3.1).
However, with the targets defined in this prior art, MRO has no
knowledge about scenarios that negatively affect KPIs out of the
scope of the MRO. In an example, reduction of mobility drops may be
experienced as a result of slowing down handovers at the expense of
degraded data throughput performance due to keeping users more in
non-favorable radio conditions. Hence, in one embodiment of the
present invention, the method extends the evaluation to include
multiple KPI that include both MRO-specific and general KPI of
interest such as data throughput performance, accessibility of the
mobile device to the WCS, and/or retainability of the mobile user
session without disruption.
[0032] FIG. 1 schematically illustrates a MRO MKD system 10
according to one configuration. The system 10 includes a MRO MKD
processor 130, which autonomously and dynamically executes
programmed operations and instructions according to the design of
the multiple KPI optimization system in certain embodiments
disclosed herein. The operational support system (OSS) 120 contains
data regarding the performance and configuration of the MCS 110.
Base station node 140 generates cellular signals 145 that permit
communication between mobile units (e.g., cellular mobile telephone
subscriber devices) and the wider telephony network. These nodes
are sometimes called "NodeB" for Third Generation MCS or "eNodeB"
for Fourth Generation MCS. The base station nodes 140 collectively
define a radio access network (RAN) 150. A communications link 160,
such as an X2 link (e.g., in 4G LTE), can be formed between
neighboring base station nodes 140.
[0033] In a further embodiment of the current invention, area
selection and special cell exclusion can be performed. In an
example, exclusions based on a site list used by important
subscribers on the MCS, indoor sites, venue specific sites, sites
bordering on the targeted area of interest, etc. can be excluded or
as specified by the MCS operator as in an imported list.
[0034] The general operation and function of the system can be
understood by analyzing multi-dimensional plots having axes
representing the configuration parameters of the system, e.g.,
time-to-trigger, hysteresis, call drop KPI, handover failures, and
other parameters. Generally, each KPI has its own dependence on the
values of the configuration parameters employed by the system. This
technique for analyzing, statistically understanding, and
controlling for the various configurations so as to control the KPI
in a dynamic fashion is an aspect of the present invention.
[0035] Call drop ratio can be determined as a function of
time-to-trigger and hysteresis (dB) in a MCS. Also handover failure
ratio can be determined as a function of time-to-trigger and
hysteresis according to some embodiments. Such failure ratios can
be taken into consideration in optimizing the design and
performance of the present system and method.
[0036] FIG. 2 illustrates an architecture showing a MCS 20
including a plurality of cells (and relations between the cells, or
cell relations) in a network 200. The cells include test cells in a
target area, poorly performing or worst performing cells, and
special case cells to be excluded (202, 204, and 206,
respectively). The cells are monitored by and exchange data with a
system 22 including one or more computer processors (desktop
computers, workstations, signal processors, etc.) 212 and one or
more data stores or databases 210. The system 22 also includes one
or more modules, engines, or instruction processing elements for
calculating solution sets 216 and KPI statistics, deviations and
other parameters and metrics 214.
[0037] In an aspect, Multiple KPI Deviations (MKD) during
optimization of the system and dynamically and iteratively tracking
this parameter space is a valuable feature of the present system
and method. By assigning appropriate values to the relevant
parameters, which can vary in space and time, the MRO MKD processor
can tune the behavior of the system for optimum performance and
robustness. Ways of using the present MKD method as implemented in
its systems include identifying best cost value per cell-period
after each configuration parameter change applied, and establishing
a reference for parameter rollback decision on the cell level after
a certain observation period of time.
[0038] FIG. 3 illustrates an exemplary flow diagram or method 40
according to one or more embodiments. The process may be divided
into four main groups of steps, but this is not limiting, as those
skilled in the art would appreciate ways to define the process or
organize the steps of the method that are equivalent or differ in
ways still comprehended by the present disclosure and invention.
Here, the main groups of steps are organized for ease of
understanding into: identifying cells or relations needing
attention 400; solution set derivation 410; learning 420; and
implementation of optimal changes 430; after which the method can
be repeated as shown.
[0039] In the steps for identifying cells or relations needing
attention 400, we can define steps to gather baseline data for a
target area 402; exclude cells or relations between cells based on
operational state 404; and identify worst performing cells or
relations between cells based on selected KPI (generally a
plurality of selected KPI) 406. In one embodiment, the system and
method can distinguish and then exclude or fix operational problems
that may interfere with the problem that is being addressed after
gathering the baseline data for the target area. For example,
problems due to physical cell identity (PCI) conflicts, where the
same identifier is assigned to more than one cell in the area,
termination point problems on the X2 link for 4.sup.th generation
MCS (e.g., communications link 160), which needs to be reset,
problems due to distant bad performing relations being added
automatically by equipment ANR functions, and other possible cases.
This allows for filtering and elimination of cells or relations
that are the victims of such common operational problems.
[0040] A specific rule per use case can be used to identify
problematic or non-optimum working areas where the above mentioned
operational problems are identified, fixed or excluded. Embodiments
of the MRO MKD system can minimize handover related radio link
failures that happen due to too late handover, too early handover,
and handover to wrong cell scenarios, while at the same time
preventing ping-pong and unnecessary handovers.
[0041] In an embodiment of the present system, the MRO may have its
own set of worst cell/relation selection rules among planned base
stations area and related buffer area. The MRO MKD processor can
target cells with the highest mobility characteristics and with
desired minimum target quality index (quality indicator KPI) levels
for best results. The MRO is also applied to the worst relations,
which are filtered. Measurement period data is used for the planned
area and buffer area. In one aspect, the planned area in the form
of worst relations will not normally be changed during the learning
period where candidate solution sets are applied to network, which
could for example be a week long. In practice, during the learning
week, the optimization area will show changes due to different
cells or cell relations satisfying criteria or filtering
thresholds, with new problematic relations arising. These additions
and deletions are not usually of the order that would affect the
overall results. These new cells and relations however will be
taken care of in the following iterations because they will become
part of the worst area in the next iteration of MRO MKD set of
calculations.
[0042] One aspect therefore includes a computer system that
improves the performance of a Mobile Communication System (MCS) in
a multi-cell network, including a processor of said computer
system, in communication with the MCS; a wireless communication
signal receiver that gathers baseline wireless communication signal
data for a target area, disposed in areas containing wireless
communication cells or wireless communication cell relations of
said MCS for which wireless communication cell or wireless
communication cell relation performance characteristics are to be
extracted based on a plurality of key performance indicators (KPI);
a computer-readable database, in data communication with said
processor, that stores baseline data received by said receiver; a
solution engine implemented in said processor and configured and
arranged to execute instructions that seed the target area by
applying a first solution set to a first wireless communication
cell and a second solution set to a second wireless communication
cell; said computer system further configured and arranged to
detect at least one performance metric for said first and second
wireless communication cells using said wireless communication
signal receiver; said computer system further configured and
arranged to store data representing said at least one performance
metric for said first and second wireless communication cells in
said database; said processor configured and arranged to calculate
a unified performance metric for said first wireless communication
cell and for said wireless communication second cell, the unified
performance metric calculated by (a) calculating respective KD
values for each of said at least one performance metric for said
first wireless communication cell and for said second wireless
communication cell and (b) calculating a MKD value for said first
wireless communication cell and for said second wireless
communication cell, said MKD value comprising a sum of said
respective KD values for said respective first or second wireless
communication cells; said processor configured and arranged to
identify a maximum MKD value and a corresponding wireless
communication cell; said processor configured and arranged to apply
the solution set of the corresponding cell to the other wireless
communication cell; and said processor configured and arranged to
generate cellular signals from a respective base station in the
first and second wireless communication cells using the solution
set of the corresponding wireless communication cell.
[0043] The processor and the processing engines (e.g., a solution
engine, a calculating processor circuit; a signal receiver and
sensor) may be implemented as best suits a particular application.
For example, a general purpose processing circuit (e.g., an
integrated circuit, etc.) or a specialty processor (e.g., graphics
processor, GPU, ASIC, etc.) may be adapted for the present purpose.
The method herein can be used to improve the performance or the
operation or to enable such processor to accomplish the present
purpose, which may be impractical or impossible using prior systems
and software.
[0044] Instructions, stored in machine-readable media can be
adapted for execution in said processor(s) so as to render the same
configured and arranged for the present application.
[0045] Still referring to FIG. 3, in the steps for derivation of a
solution set 410, we can define steps to calculate multiple KPI
deviation 412 and to tune solution sets, and specify parameter
changes for the specific period 414. A notable aspect of the
present system and method is that solution sets can be derived for
a MCS using multiple KPI and changes in said multiple KPI metrics
to identify best solution sets. In an aspect, the solution set
derivation is dynamic and time variable, in an example, over
defined time periods in a day or other periodicity. In another
aspect, the solution set derivation can be local in nature, at the
cell level in a cellular system, and therefore have global
performance and robustness implications.
[0046] It has been suggested above that the present method and
system can be automated, which in some embodiments can employ
techniques of machine or assisted learning, artificial intelligence
techniques, or other helpful automation and optimization
strategies. In the steps for learning 420, we can define steps to
apply solution sets for each daily period 422, which is generalized
to any periodicity that is sensible for a given application; and
rollback changes if a cell multiple KPI deviation is degraded more
than a rollback threshold 424. A well-defined set of optimization
changes can be applied on selected worst region for a specified
period of time. Results can then be evaluated to find the optimum
set to be applied on cell/relation level.
[0047] In an aspect, the present system and method can find and
tune solution sets iteratively, possibly holding the permanent
introduction of the found solution sets into the operation of the
MCS until the following stage of the process. In this way, the
operator and/or system can `watch` for the impact of the updated
solution sets on the system and decide if and when to implement the
same on a permanent basis.
[0048] In the steps of the present method 40 for the implementation
of optimal changes 430, we define steps to identify a cell's
optimum parameter setting level identified per period 432; applying
the setting for highest KPI performance in the target area 434; and
rollback based on changes in multiple KPI statistics 436. In an
aspect, the results of the learning process above can be
implemented into the daily operation of the MCS after the operator
or system is satisfied with the revised solution sets. This may
include a statistical study of the actual or predicted impact of
such solution sets on the multiple KPI in use.
[0049] In another embodiment, the steps above can be combined or
further divided into sub-processes as suits a given application,
and some simplification and generalization is inevitable for the
sake of disclosure. Nonetheless, those skilled in the art would
appreciate a number of aspects of the present disclosure and
exemplary embodiments. In an aspect, it is understood that the
above steps could be carried out fully or partially automatically
in or by a machine such as a computer or processing apparatus.
Typically, such a machine would have circuitry and carry or be
adapted to execute stored machine-readable instructions (sometimes
encoded into transitory or non-transitory data storage and memory
units). In addition to processing and data storage capability, the
machine would also typically be equipped with network communication
functionality such as input/output ports for receiving and sending
electronic signals over such a network. In some aspects, the
Internet could be such a network. In other aspects, a wired or
wireless telephony network could be connected thereto.
[0050] In further embodiments of the present invention, a user
interface may be included in the system so that human users or
computer interfaces can provide and receive information exchanged
with the system described here. The user interface can include
visual and/or audible outputs indicative of relevant information
being presented by the system. Graphical depictions of the
performance and robustness of the system or MCS it is monitoring
and controlling can be displayed and actions can be taken in
response thereto. Also, alarm units signaling some pre-determined
condition or programmed alarm criterion can be included in the
hardware or software of the system. A database unit can be included
with or be accessible to the system in which data is stored such as
detailed measurement results, data tracking performance and
robustness, and other data that can be used for future learning or
programming of the system.
[0051] FIG. 4 illustrates an exemplary and simplified flow diagram
50 of the phases of operating the present MRO MKD system and method
in a MCS. The process depicted can have a total time frame of about
one week in an embodiment.
[0052] In phase 500 we implement the initial solution set or a
default set. A solution set herein is a combination of one or more
configuration parameter settings. The implementation of the
parameter settings is again intended to improve overall user and
operator experience and quality. This can be based on evaluation of
a number of KPI and the solution sets therefore being based on
conditions that are satisfied. The conditions can be built-in from
prior knowledge from experience and measurements in the field. The
implementation includes generating cellular signals from the base
station nodes using the initial solution set or default set. The
cellular signals allow for wireless communication between mobile
units (e.g., cellular mobile telephone subscriber devices) and the
wider telephony network.
[0053] In phase 510 we make measurements, collecting baseline data,
still using the initial solution set. Statistical information for
baseline in a typical cellular MCS can be acquired over a month or
so of observation (but this is a general statement and good data
can be accumulated in two weeks, in most cases). Measurements are
done using cellular and relational performance management data with
some minimum resolution for the planned area and buffer area that
are collected and stored in this phase.
[0054] In an example, each day is divided into three periods: a
morning period (P1) from midnight until 8 a.m., a daytime period
(P2) from 8 a.m. until 4 p.m., and an evening period (P3) from 4
p.m. until midnight. Of course other periods may also be defined.
Data is collected and measurements made in the system, then
analysis and calculations are performed on data measured from each
of the periods of the day. The data can be organized and labeled,
for example, referring to "P1W1D1 " at Period P1 of Week 1 and Day
1, and so on.
[0055] In some aspects, data can be excluded from outlying or
special times, days or events so as to avoid contaminating the
normal data collection and analysis with data from statistical
outliers.
[0056] In phase 520 the system is seeded by applying any determined
solution set results and rolling back any changes after an hourly
analysis of the performance of the system (or any other convenient
periodicity). In phase 530 we apply the best solution set for a
current iteration and then roll back the parameters after hourly
analysis (or any other periodicity), if the rollback conditions are
satisfied.
[0057] The three daily period system described above can be used
for seeding the system. Again, the period count and duration are
configurable and only exemplary. For a day period, solution sets
can be applied for different periods taking into account weekday
and weekend days as having different behaviors in some cases. In
one scenario, weekend days may be returned to the initial solution
sets while solution sets are applied during weekdays. Seeding may
extend from one to two weeks in some embodiments. The following
table (Table I) shows an example of the seeding process over five
days (D) having three periods (P) each, wherein sample seeding is
done using two solution sets (SS) in a one week period applied in
MRO worst areas.
TABLE-US-00001 TABLE I D 1 D 2 D 3 D 4 D 5 P1 SS01 SS01 SS02 SS02
SS01 P2 SS01 SS01 SS02 SS02 SS01 P3 SS01 SS01 SS02 SS02 SS01
[0058] The following table (Table II) shows sample seeding for four
solution sets in one week period applied to MRO in worst areas. In
an embodiment, the solution sets can be returned to the initial or
default set at the end of the weekday.
TABLE-US-00002 TABLE II D 1 D 2 D 3 D 4 D 5 P1 SS01 SS02 SS03 SS04
SS01 P2 SS01 SS02 SS03 SS04 SS01 P3 SS01 SS02 SS03 SS04 SS01
[0059] Rollback triggers can be in effect to avoid adverse effects
to network KPI. If after applying a solution set the cell MKD
degraded by more than a rollback threshold amount (e.g., 3*delta)
for some defined increment delta, this solution set can be rolled
back and reverts to the reference set within the next hour or next
period.
[0060] The present system and method are capable of determining a
best solution set implementation. Optimum parameter settings are
identified for each period and applied to the MCS, resulting in
best KPI performance on a regional level. The best solution set
value is applied for a weekday or weekend configuration. The
following table (Table III) shows an example of identification of a
best multiple KPI deviation at the end of a seeding phase of
operation. The underlined solution sets show the best MKD solution
set and day/period combinations.
TABLE-US-00003 TABLE III Cell 1 D1 D2 D3 D4 D5 Best MKD P1 SS01
SS02 SS03 SS01 SS02 SS03 P2 SS01 SS02 SS03 SS01 SS02 SS01 P3 SS01
SS02 SS03 SS01 SS02 SS02
[0061] In some embodiments, cell or relation based rollbacks may be
applied only for the day the best solution set is applied (e.g.,
Monday). After Monday, region-based MKD evaluation can be set and a
warning report can be generated in case region-based KPI degrade by
more than a rollback threshold. (e.g., some
5*delta_standard_region). Of course, the specific examples above
are only for the sake of illustration and are not limiting.
[0062] Therefore, the present system and method have a number of
inventive features, some or all of which can improve the
performance and robustness of a MCS in various embodiments. The MCS
may collect and analyze and use system parameter and configuration
information to optimize the performance and robustness of the MCS.
The system may learn by statistical observation and iterative
determination of best solution sets and then apply solution sets
found to best affect the MCS at a cell level and in a dynamic
fashion based on time periods of the day (or other period). Changes
may be rolled back if certain criteria are not met by the changes.
In one embodiment, this may be applied in hardware and/or software
so that it forms an automated closed-loop means for determining an
optimum set of conditions for operating a MCS to increase the
quality of the network, reduce dropped calls, improve handover
behavior, and other advantages.
[0063] In a certain embodiment of the present invention, the system
and method can be deployed as stated using a multiple KPI deviation
(MKD) method of calculation. A cost function can be computed in
some embodiments to minimize handover (HO) failure rates due to
"ping-ponging" of a unit, excessively early or too late handover,
or handover to a wrong cell. This can be implemented for handover
success rate KPI improvements. For any day (D) and period (P) of
the day we can define KPI directed to such improvements, presented
as exemplary KPI, whereas one of skill in the art can appreciate
other such KPI:
[0064] P1AvgW1WN signifies P1 average for Week1 to WeekN; and
[0065] P1StdevW1WN signifies P1 standard deviation for Week1 to
WeekN.
[0066] Embodiments of the present MKD method may include
identifying best cost value per period. In an example, Cell A has a
best benefit when the corresponding parameter is applied to it
during period P1 in a week. In another example, the MKD method is
applied for establishing a reference for rollback decisions made on
a cell or relation level after some evaluation time interval (for
example 60 minutes). More specifically, a rollback to a reference
base parameter configuration may happen after applying a solution
set for Cell A, and MKD for a given period (e.g., 9:00 to
10:00o'clock) becomes bigger than or equal to
(P1AvgW1WN-rollback_coefficient*P1StdevW1WN). In an example,
rollback coefficient can have the value of five (5).
[0067] The present system and method can be deployed using a cell
MKD, or cell-based multiple KPI standard deviation from a
calculated reference. Here the MKD can be used for best solution
set evaluation and rollback decisions, if the solution set is
directed to cell based configuration parameters. Cell based MKD can
include KPI in the form of cells and relations. For relational KPI,
aggregation from relations at the cell level can be performed. In
some embodiments, each KPI has a "class" attached, for example,
"important," "sustain," and "degrade." These classes have their
respective weights where a KPI can also have a separate weighting
within a class. Overall weight is indicated as multiplication of
KpiWeight*ClassWeight.
[0068] In yet another aspect, the MKD can be relation-based on a
multiple KPI standard deviation from a calculated reference. This
MKD will be used for best solution set evaluation and rollback
decisions, if the solution set includes relational configuration
parameters. In an example, relation based MKD can include the
following components during calculation of said deviations:
relation based KPI changes from source cell to neighbor cell (Cell
to Ncell); Cell based KPI changes in source cell (Cell); and Cell
based KPI changes in neighbor cell (Ncell).
[0069] In yet another aspect of the present invention, inputs to
MKD method are KPI metrics for cell based calculations. In an
example, the following cell based metrics ERab % Retain, HoExe %
SucclntraF, Ho % RLF, CSSR, EthputUserDL, Ho % Osc, and/or
HoExeAttlntraF and/or relational metrics, HoExe % SucclntraF,
HoExeAttlntraF, and/or Ho % RLF, may be used in the MKD method.
ERab % Retain gives the ratio of normal call completions to all
(normal and abnormal) call completions. HoExe % SucclntraF gives
ratio of successful handover events to all handover execution
attempts within intrafrequency relations. Ho % RLF gives the ratio
of radio link failures due to mobility problems to handover
execution attempts. CSSR gives call setup completion success ratio.
EthputUserDL gives average user throughput in downlink experienced
under the cell. Ho % Osc gives ratio of returned handovers within a
predefined short period of time (namely ping pong handover) to all
handover execution attempts. HoExeAttlntraF gives handover
execution attempts for anintrafrequency relation of a cell. HoExe %
SucclntraF, HoExeAttlntraF and Ho % RLF may be calculated both on a
cell aggregated or an individual cell relation level.
[0070] In an example, a unified performance metric is calculated as
a measure to assess how much standard deviation occurred in terms
of said input KPIs in aggregate terms as given in a sample
cell-based MKD Calculation illustrated in table 60 of FIG. 5.
[0071] OverallWeight gives desired weighting factor between KPIs
which is predefined based on MCS. KPIDir is KPI direction which
identifies the desired direction of change in terms of positive or
negative. P1Avg[D1-D5] and P1Stdev[D1-D5] gives average and
standard deviation calculations for corresponding KPIs during a
baseline period. KPIs Current Values are collected from MCS.
RawKPIDev is defined as (Current Value-P1Avg[D1-D5])/P1
Stdev[D1-D5]. NormKpiDev and KD refers to (RawKpiDev*KpiDir) and
(NormKpiDev*OverallWeight) respectively. MKD is the sum of all KD
components, In the example below, ERab % Retain, HoExe %
SucclntraF, Ho % RLF, EthputUserDL contribute positively to overall
MKD whereas Ho % Osc and HoExeAttIntraF contribute negatively due
to increase in handover execution attempts and handover
oscillation.
[0072] Output from the MKD processor has two main uses: (1)
Performance gain measure (e.g. MKDmax) to select the best solution
set and (2) Configuration revert-back decision, in case performance
gain is below a specified threshold (e.g. MKDmin). Unexpected KPI
dependencies may degrade system performance. Using KPI-based
coefficients during KPI deviation aggregations, larger weights may
be assigned to specific KPI based on field experience and
preferences (e.g. Drop rate improvement, handover attempt
increase/decrease, HO success rate improvement and/or degradation)
whereas smaller weights may also be assigned to other KPIs of
interest to get a measure of unexpected but loosely-coupled KPI
deviations. (e.g., call setup success rate changes).
[0073] The present MKD method and system provides flexibility to
inject a field knowledge base by allowing the assignment of
coefficients to each KPI during this automatic assessment process.
In this way, not only use-case specific KPIs are evaluated but also
other KPIs may be considered in the assessment.
[0074] The present invention should not be considered limited to
the particular embodiments described above, but rather should be
understood to cover all aspects of the invention as fairly set out
in the attached claims. Various modifications, equivalent
processes, as well as numerous structures to which the present
invention may be applicable, will be readily apparent to those
skilled in the art to which the present invention is directed upon
review of the present disclosure. The claims are intended to cover
such modifications and equivalents.
* * * * *